Due to some practical limitations, it is not possible to adopt images which have both high spectral and spatial resolutions. Image FUSION is one of the methods that utilizes image supplementary INFORMATION, to the effect that it combines the spatial INFORMATION of images with high spatial resolution, By the use of these spectral INFORMATION of the images with high spectral resolution, one can creates an image that has high spatial and spectral resolutions simultaneously. Current methods and algorithms of image FUSION are not efficient enough to combine new satellite images due to some changes in these satellites; therefore, providing new methods of image FUSION is of paramount significance for the above-mentioned satellites. Image FUSION methods should desirably maintain the spectral and spatial INFORMATION of the original images. In the present study, a new algorithm was introduced to combine the spatial INFORMATION of the IKONOS images with the spatial resolution of 1m with the spectral INFORMATION of the SPOT images with the spatial resolution of 20m. This algorithm is in the feature-level and is based on the Retinal Model. Other existent FUSION models such as IHS, HPF, PCA, Wavelet and the hybrid Wavelet-PC were also applied to these images. The results of the spatial and spectral assessments of the combined images indicated that the spectral and spatial INFORMATION of the proposed method are better. One of the major advantages of this method is that there is no need for resampling, which is a must in other methods.

A new concept named pseudo INFORMATION measure is introduced. By this measure, Bayesian FUSION of independent sources of INFORMATION is extended to a wide range of possible formulations and some new FUSION formulas are calculated. The coincidence between the performance of the proposed method of FUSION with the results that are expected by human logic and output sensitivity of the FUSION process are discussed. Also, we have discussed the resulting flexibility for map building applications. Map building by using the proposed FUSION formulas has been implemented on Khepera robot. The resulting map were fed to "A*" path planning algorithm for comparative purposes. For the resulting routes, two factors are considered: length and a danger measure which is an increasing function of the least distance of the path to obstacles. The results show that by using the proposed FUSION formulas, more informative maps of the environment are obtained by which more appropriate routes are achieved. Based on the selected function, there is a trade-off between the length of the resulting routes and their safety. This flexibility lets us choose the right FUSION function for different map building applications.

In this paper we have proposed an approach for emotion detection in implicit texts. We have introduced a combinational system based on three subsystems. Each one analyzes input data from a different aspect and produces an emotion label as output. The first subsystem is a machine learning method. The second one is a statistical approach based on vector space model (VSM) and the last one is a keyword-based subsystem with an INFORMATION FUSION component to aggregate the final output of main system. We analyzed the performance of our proposed system on ISEAR dataset with seven emotions: anger, joy, sad, shame, fear, disgust and guilt. The results show that our combinational system outperforms each subsystem with overall f-measure of 0.68 and at least up to 0.71 in terms of F1 in emotion level except for anger. The overall performance of the main system is 9.13% better than machine learning module, 16.6% better than VSM and 23% better than keyword-based.

INFORMATION FUSION compared with Data FUSION concept stands in a higher level of hierarchy on decision making process. As a result, this makes it possible to achieve better and more complete results.Today intelligent systems are able to act as personal assistant of human, and provide the needed INFORMATION among the available INFORMATION sources. The research work on this field is essential and there are still many deficiencies on this respect to be sorted out. In this article, we have focused on the INFORMATION FUSION concept with a novel approach to design and implement an architecture of a customized intelligent agent, which will be able to retrieve the INFORMATION needed by user with respect to his/her interests from various INFORMATION servers (such as internet and intranet search engines and databases). The designed agent uses the different INFORMATION FUSION techniques to achieve this goal. The OWA operator which is the subset of the great family of Fuzzy Integral Operators is used to design the operational model of above mentioned agent. This agent fuses the Data, Features, and Decisions in three distinct levels.The agent is able to cluster categories, and extracts the behavioral model of INFORMATION servers with respect to each cluster and uses these models in final decision making process.

Philadelphia chromosome can be founding 95% of patients with chronic myeloid leukemia (CML) by cytogenetic studies. The fused bcr/abl is transcribed in two types of chimeric mRNA. RT-PCR amplification of these two transcripts have been designed to give two different size products. This assay can detect one positive bcr/abl expressing cell in a back ground of 106 negative bcr/abl cells. The power of this assay is the detection of minimal residual disease (MRD) between 6-12 months following bone marrow transplantation (BMT) is an independent and significant factor that predicts the relapse in future. The aim of optimization was to detect β2 microglobulin (β 2M) mRNA. Detection of β2 mRNA and absence of bcr/abl indicates that the patient is negative for MRD and as a result, there is molecular remission in addition to clinical remission. To monitor MRD we tested a patient's blood sample who had tolerated allogenic BMT 7 years ago. bcr/abl wasn't detected in this patient and only β2 M was observed. This result confirmed the absence of MRD in this case. To effectively monitor minimal leukemic activity after BMT, we used a competitive RT- PCR to quantify expression of the characteristic bcr/abl FUSION gene mRNA in patients with CML.    

The diverse applications and widespread effects of cyberspace in most military and civilian applications have led to the rapid growth of data, INFORMATION, knowledge, technology, methods, tools, and cyber systems. One of the important and strategic requirements in the cyber command and control cycle is the capability to extract, process, FUSION and analyze data and INFORMATION from various sources to achieve the desired situational awareness of the cyberspace and cyber operations environment. Considering the various aspects of this issue, the use of soft data, ie data and INFORMATION from human resources, along with hard data, ie data and INFORMATION of machine resources, can help in achieving more accurate and reliable recognition and decision. One of the main research topics in this area is designing a conceptual model and appropriate processing framework for analysis and inference based on data and INFORMATION with the ability of linking various INFORMATION from different sources with each other and modeling different types of uncertainty in hard and soft data and fusing them. This paper presents an ontology-based approach to the processing and FUSION of hard and soft data in cyber command and control, in which various aspects of the issue has been addressed including ontology-based architecture and processing framework of INFORMATION inference and FUSION, the method of representation of uncertainty and reliability in hard and soft data and fusing these data, conversion of beliefs into probabilities to be able to make decisions on the hypotheses under consideration, designing an appropriate ontology model for cyber command and control purposes, and design and implementation of ontology-based logic of inference and FUSION. The results of applying the proposed model in a typical scenario of cyber command and control show that it is operational in FUSION of hard and soft cyber data. In addition to the ability of inference and FUSION, one of the notable features of the proposed approach is its scalability and adaptability to new extensions in the tools and requirements of cyber command and control.